Big data technologies have changed how we use and analyze vast amounts of data. They’ve paved the way for new solutions. These include analytics streams that are automated and efficient. They improve operations. AI and machine learning can analyze vast data streams at superhuman speeds. They unlock a trove of insights. These tools are vital for industries like logistics and entertainment. For example, https://www.playamo.com uses data to personalize users’ experiences.
New forms of information and customer profiles are now possible. They can be segmented and categorized by age, location, and engagement levels. It’s now possible to track and measure how interested or engaged your audience truly are.
But, for business, especially small to medium firms, location data is very useful.
Why Location Intelligence Matters
Data sets are very valuable and full of potential. There’s no denying it. With the right info, you can know your customers’ wants. You can also gauge your business and products. This will help you make better, more predictive decisions.
Crucial to truly understanding context, yet, is the idea of “where” or “when” a series of events comes to pass. Most business intelligence systems let you see a customer’s purchases. You can see details like buy prices, timestamps, and payment methods. But, like most businesses, it’s important to know where that buy was made — the store — and how they paid. Did the customer visit a local store? Did the customer make the buy while traveling, either out of town or abroad? Did they make the buy online?
This information can also add depth to the data you’ve already collected. You see, visualization is needed to understand large data sets. This process is often referred to as “turning your business data into maps.” But, this is only possible with location info on data streams or datasets. Location intelligence links data to specific geographic locations, usually marked by coordinates.
Once you have a location attached, you can present or view the data in new, more revealing ways. This explains why location data is vital. It helps you understand regions and their effects on your business.
Location intelligence will improve marketing, management, and support teams. It will also help supply chain and logistics teams.
Where Does AI Fit In?
AI and machine learning platforms can analyze huge data sets. They can do this quickly and accurately. These technologies are promising. But, where does the “learning” in machine learning come from? They are improving over time as they ingest more data. They are already very accurate and useful. They improve with each use of the technology.
It’s why platforms like IBM’s Watson are not improving. They’re being used for new, innovative things outside their original purpose. A data analysis system can now create film trailers, play games, and aid medical research.
In location intelligence, AI and big data are used to build advanced models. They will have unmatched accuracy. These algorithms (or frameworks, if you will) can then be used to take action or deploy a solution.
Imagine using large data sets in business. You could know when, where, and how to maximize revenue and success. To make this less abstract, let’s look at a real-world example.
It’s hurricane season. On the East Coast, many are stocking up and preparing their homes and families. Retailers and businesses can use this to deliver the right products. It will increase supply to meet demand.
But, imagine knowing how climate, weather, and human behavior affect business. With access to current and historical geographical data, businesses can find insights. They can use them in today’s dynamic market. For instance, a business could predict the exact demand for a product. It could know where and when it would peak. It could also see how these factors would affect production and distribution. It would affect scaling decisions, too. This foresight lets companies be proactive, not reactive. It ensures they run efficiently and waste little.
Moreover, the true power of such algorithms lies in their adaptability and reusability. A validated algorithm is a lasting asset. It provides accurate insights. It can be reused to tackle similar future challenges. It can also be adapted for very different scenarios or market events. It boosts efficiency and adds agility to business strategies. This lets organizations pivot to changing conditions.
AI and machine learning are changing how data scientists and analysts solve problems. These technologies allow the creation of efficient, scalable frameworks for using data. They make it easier than ever for businesses to harness their data’s full potential. The result is not better decisions, but a competitive edge in a data-driven world.
James is the head of marketing at Tamoco
